Designing an Audiocast Assignment: A Primary-Literature-Based Approach that Promotes Student Learning of Cell and Molecular Biology through Conversations with Scientist Authors
Bibliographic record
Abstract
We believe that conversations between students and scientist authors that link recently published research to fundamental concepts taught in undergraduate biology courses can serve to engage students and enhance learning. To explore this hypothesis, we designed an assignment in a 2nd year cell and molecular biology course in which students read a scientific article, conduct an interview with the corresponding author of the publication, and then produce an audiocast (or videocast). The audiocast summarizes the paper’s findings and describes how the research advance links back to fundamental concepts discussed in the course and its implications for the field. Feedback from student surveys has been positive and suggests that students felt they developed important analytical skills and a better understanding of the process of science through participation in this assignment. Students enjoyed the interactions with scientists and reported on how their learning from primary literature was enriched by asking questions of the authors. Importantly, the assignment had a very positive influence on student attitudes towards research; this is increasingly important at a time when public involvement in debates about scientific funding cuts is critical. We hope this assignment will be of interest to other instructors that teach undergraduate foundation courses in the life sciences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".